FlowPure: Continuous Normalizing Flows for Adversarial Purification

📰 ArXiv cs.AI

FlowPure uses continuous normalizing flows for adversarial purification in machine learning models

advanced Published 31 Mar 2026

Action Steps

Implement continuous normalizing flows to model the distribution of adversarial examples
Use the forward process to inject Gaussian noise and dilute adversarial perturbations
Apply the reverse process to purify the input data and improve model robustness
Evaluate the effectiveness of FlowPure in removing adversarial perturbations and improving model accuracy

Who Needs to Know This

ML researchers and engineers working on adversarial robustness can benefit from this approach to improve model security and reliability

Key Insight

💡 Continuous normalizing flows can be used to effectively remove adversarial perturbations and improve model robustness

Key Takeaways

FlowPure uses continuous normalizing flows for adversarial purification in machine learning models

Full Article

Title: FlowPure: Continuous Normalizing Flows for Adversarial Purification

Abstract:
arXiv:2505.13280v2 Announce Type: replace-cross Abstract: Despite significant advances in the area, adversarial robustness remains a critical challenge in systems employing machine learning models. The removal of adversarial perturbations at inference time, known as adversarial purification, has emerged as a promising defense strategy. To achieve this, state-of-the-art methods leverage diffusion models that inject Gaussian noise during a forward process to dilute adversarial perturbations, follo

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